Benchmarking reports

from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	13.0	52.823419
daal4py_KNeighborsClassifier	0.0	3.0	3.545643
KNeighborsClassifier_kd_tree	0.0	6.0	39.438890
daal4py_KNeighborsClassifier_kd_tree	0.0	1.0	43.309989
KMeans_tall	0.0	1.0	39.000246
daal4py_KMeans_tall	0.0	1.0	15.023345
KMeans_short	0.0	0.0	16.721461
daal4py_KMeans_short	0.0	0.0	7.369816
LogisticRegression	0.0	1.0	3.427299
daal4py_LogisticRegression	0.0	0.0	53.872494
Ridge	0.0	0.0	47.123034
daal4py_Ridge	0.0	0.0	14.009139
total	0.0	31.0	35.739804

KNeighborsClassifier vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier	fit	0.133	0.001	1000000	1000000	100	brute	-1	1	NaN	NaN	0.482	0.001	0.276	0.002	See
1	KNeighborsClassifier	predict	0.155	0.014	1000000	1	100	brute	-1	1	1.0	0.0	0.089	0.000	1.743	0.160	See
2	KNeighborsClassifier	predict	27.947	0.314	1000000	1000	100	brute	-1	1	1.0	0.0	1.745	0.012	16.020	0.209	See
3	KNeighborsClassifier	fit	0.131	0.002	1000000	1000000	100	brute	-1	5	NaN	NaN	0.484	0.005	0.270	0.006	See
4	KNeighborsClassifier	predict	0.165	0.008	1000000	1	100	brute	-1	5	0.0	1.0	0.088	0.002	1.871	0.104	See
5	KNeighborsClassifier	predict	37.294	0.000	1000000	1000	100	brute	-1	5	0.0	1.0	1.765	0.007	21.128	0.088	See
6	KNeighborsClassifier	fit	0.119	0.000	1000000	1000000	100	brute	-1	100	NaN	NaN	0.483	0.001	0.247	0.001	See
7	KNeighborsClassifier	predict	0.170	0.014	1000000	1	100	brute	-1	100	0.0	1.0	0.089	0.000	1.898	0.161	See
8	KNeighborsClassifier	predict	36.971	0.000	1000000	1000	100	brute	-1	100	0.0	1.0	1.805	0.009	20.481	0.103	See
9	KNeighborsClassifier	fit	0.120	0.000	1000000	1000000	100	brute	1	1	NaN	NaN	0.486	0.003	0.247	0.002	See
10	KNeighborsClassifier	predict	0.175	0.001	1000000	1	100	brute	1	1	1.0	0.0	0.088	0.000	1.985	0.012	See
11	KNeighborsClassifier	predict	13.499	0.037	1000000	1000	100	brute	1	1	1.0	0.0	1.758	0.009	7.679	0.045	See
12	KNeighborsClassifier	fit	0.120	0.000	1000000	1000000	100	brute	1	5	NaN	NaN	0.484	0.001	0.247	0.001	See
13	KNeighborsClassifier	predict	0.186	0.003	1000000	1	100	brute	1	5	0.0	1.0	0.090	0.000	2.066	0.032	See
14	KNeighborsClassifier	predict	23.610	0.023	1000000	1000	100	brute	1	5	0.0	1.0	1.747	0.007	13.513	0.059	See
15	KNeighborsClassifier	fit	0.132	0.003	1000000	1000000	100	brute	1	100	NaN	NaN	0.487	0.004	0.271	0.007	See
16	KNeighborsClassifier	predict	0.184	0.001	1000000	1	100	brute	1	100	0.0	1.0	0.089	0.001	2.066	0.022	See
17	KNeighborsClassifier	predict	23.556	0.028	1000000	1000	100	brute	1	100	0.0	1.0	1.838	0.063	12.816	0.441	See
18	KNeighborsClassifier	fit	0.056	0.001	1000000	1000000	2	brute	-1	1	NaN	NaN	0.101	0.002	0.558	0.013	See
19	KNeighborsClassifier	predict	0.021	0.002	1000000	1	2	brute	-1	1	1.0	1.0	0.006	0.000	3.763	0.541	See
20	KNeighborsClassifier	predict	25.222	0.073	1000000	1000	2	brute	-1	1	1.0	1.0	0.258	0.001	97.780	0.477	See
21	KNeighborsClassifier	fit	0.057	0.000	1000000	1000000	2	brute	-1	5	NaN	NaN	0.100	0.002	0.572	0.015	See
22	KNeighborsClassifier	predict	0.029	0.002	1000000	1	2	brute	-1	5	1.0	1.0	0.005	0.000	5.412	0.454	See
23	KNeighborsClassifier	predict	33.013	0.000	1000000	1000	2	brute	-1	5	1.0	1.0	0.261	0.002	126.602	0.829	See
24	KNeighborsClassifier	fit	0.057	0.001	1000000	1000000	2	brute	-1	100	NaN	NaN	0.101	0.001	0.563	0.011	See
25	KNeighborsClassifier	predict	0.029	0.003	1000000	1	2	brute	-1	100	1.0	1.0	0.005	0.000	5.388	0.661	See
26	KNeighborsClassifier	predict	33.103	0.000	1000000	1000	2	brute	-1	100	1.0	1.0	0.306	0.002	108.076	0.530	See
27	KNeighborsClassifier	fit	0.056	0.000	1000000	1000000	2	brute	1	1	NaN	NaN	0.100	0.002	0.564	0.015	See
28	KNeighborsClassifier	predict	0.014	0.001	1000000	1	2	brute	1	1	1.0	1.0	0.005	0.000	2.817	0.282	See
29	KNeighborsClassifier	predict	10.516	0.021	1000000	1000	2	brute	1	1	1.0	1.0	0.258	0.001	40.821	0.211	See
30	KNeighborsClassifier	fit	0.056	0.001	1000000	1000000	2	brute	1	5	NaN	NaN	0.100	0.001	0.559	0.012	See
31	KNeighborsClassifier	predict	0.023	0.001	1000000	1	2	brute	1	5	1.0	1.0	0.005	0.001	4.354	0.456	See
32	KNeighborsClassifier	predict	18.674	0.010	1000000	1000	2	brute	1	5	1.0	1.0	0.261	0.002	71.579	0.646	See
33	KNeighborsClassifier	fit	0.056	0.001	1000000	1000000	2	brute	1	100	NaN	NaN	0.100	0.002	0.563	0.015	See
34	KNeighborsClassifier	predict	0.023	0.001	1000000	1	2	brute	1	100	1.0	1.0	0.006	0.000	4.239	0.388	See
35	KNeighborsClassifier	predict	18.711	0.010	1000000	1000	2	brute	1	100	1.0	1.0	0.306	0.001	61.057	0.238	See

Plots

Shared hyperparameters:

	value
algorithm	brute

KNeighborsClassifier_kd_tree vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier_kd_tree	fit	2.955	0.029	1000000	1000000	10	kd_tree	-1	1	NaN	NaN	0.740	0.012	3.995	0.075	See
1	KNeighborsClassifier_kd_tree	predict	0.002	0.001	1000000	1	10	kd_tree	-1	1	1.0	1.0	0.000	0.000	8.328	5.530	See
2	KNeighborsClassifier_kd_tree	predict	0.490	0.007	1000000	1000	10	kd_tree	-1	1	1.0	1.0	0.119	0.003	4.130	0.105	See
3	KNeighborsClassifier_kd_tree	fit	2.951	0.042	1000000	1000000	10	kd_tree	-1	5	NaN	NaN	0.764	0.012	3.863	0.082	See
4	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	10	kd_tree	-1	5	1.0	1.0	0.000	0.000	5.981	3.826	See
5	KNeighborsClassifier_kd_tree	predict	0.866	0.011	1000000	1000	10	kd_tree	-1	5	1.0	1.0	0.214	0.005	4.046	0.107	See
6	KNeighborsClassifier_kd_tree	fit	2.950	0.033	1000000	1000000	10	kd_tree	-1	100	NaN	NaN	0.720	0.010	4.099	0.075	See
7	KNeighborsClassifier_kd_tree	predict	0.004	0.001	1000000	1	10	kd_tree	-1	100	1.0	1.0	0.001	0.000	4.890	2.493	See
8	KNeighborsClassifier_kd_tree	predict	3.029	0.018	1000000	1000	10	kd_tree	-1	100	1.0	1.0	0.628	0.009	4.826	0.073	See
9	KNeighborsClassifier_kd_tree	fit	3.016	0.061	1000000	1000000	10	kd_tree	1	1	NaN	NaN	0.765	0.015	3.942	0.111	See
10	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	10	kd_tree	1	1	1.0	1.0	0.000	0.000	3.030	1.623	See
11	KNeighborsClassifier_kd_tree	predict	0.860	0.007	1000000	1000	10	kd_tree	1	1	1.0	1.0	0.122	0.002	7.062	0.144	See
12	KNeighborsClassifier_kd_tree	fit	3.072	0.112	1000000	1000000	10	kd_tree	1	5	NaN	NaN	0.754	0.008	4.072	0.155	See
13	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	10	kd_tree	1	5	1.0	1.0	0.000	0.000	3.066	1.869	See
14	KNeighborsClassifier_kd_tree	predict	1.607	0.010	1000000	1000	10	kd_tree	1	5	1.0	1.0	0.226	0.005	7.120	0.150	See
15	KNeighborsClassifier_kd_tree	fit	2.955	0.013	1000000	1000000	10	kd_tree	1	100	NaN	NaN	0.772	0.007	3.828	0.037	See
16	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	10	kd_tree	1	100	1.0	1.0	0.001	0.000	3.280	1.707	See
17	KNeighborsClassifier_kd_tree	predict	5.488	0.059	1000000	1000	10	kd_tree	1	100	1.0	1.0	0.634	0.007	8.656	0.136	See
18	KNeighborsClassifier_kd_tree	fit	1.235	0.009	1000000	1000000	2	kd_tree	-1	1	NaN	NaN	0.464	0.015	2.661	0.085	See
19	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	2	kd_tree	-1	1	1.0	1.0	0.000	0.000	18.482	16.424	See
20	KNeighborsClassifier_kd_tree	predict	0.025	0.001	1000000	1000	2	kd_tree	-1	1	1.0	1.0	0.001	0.000	32.141	15.053	See
21	KNeighborsClassifier_kd_tree	fit	1.206	0.021	1000000	1000000	2	kd_tree	-1	5	NaN	NaN	0.458	0.014	2.630	0.091	See
22	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	2	kd_tree	-1	5	1.0	1.0	0.000	0.000	19.762	17.922	See
23	KNeighborsClassifier_kd_tree	predict	0.026	0.001	1000000	1000	2	kd_tree	-1	5	1.0	1.0	0.001	0.000	23.645	9.543	See
24	KNeighborsClassifier_kd_tree	fit	1.196	0.009	1000000	1000000	2	kd_tree	-1	100	NaN	NaN	0.453	0.014	2.639	0.085	See
25	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	2	kd_tree	-1	100	1.0	1.0	0.000	0.000	18.164	15.177	See
26	KNeighborsClassifier_kd_tree	predict	0.045	0.001	1000000	1000	2	kd_tree	-1	100	1.0	1.0	0.006	0.001	7.221	0.930	See
27	KNeighborsClassifier_kd_tree	fit	1.192	0.006	1000000	1000000	2	kd_tree	1	1	NaN	NaN	0.457	0.017	2.611	0.097	See
28	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	1	1.0	1.0	0.000	0.000	5.474	5.125	See
29	KNeighborsClassifier_kd_tree	predict	0.024	0.000	1000000	1000	2	kd_tree	1	1	1.0	1.0	0.001	0.000	30.974	14.041	See
30	KNeighborsClassifier_kd_tree	fit	1.199	0.007	1000000	1000000	2	kd_tree	1	5	NaN	NaN	0.451	0.012	2.657	0.073	See
31	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	5	1.0	1.0	0.000	0.000	5.486	5.208	See
32	KNeighborsClassifier_kd_tree	predict	0.025	0.001	1000000	1000	2	kd_tree	1	5	1.0	1.0	0.001	0.001	21.965	11.809	See
33	KNeighborsClassifier_kd_tree	fit	1.204	0.013	1000000	1000000	2	kd_tree	1	100	NaN	NaN	0.448	0.015	2.685	0.093	See
34	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	100	1.0	1.0	0.000	0.000	5.117	4.571	See
35	KNeighborsClassifier_kd_tree	predict	0.056	0.001	1000000	1000	2	kd_tree	1	100	1.0	1.0	0.007	0.001	8.389	1.358	See

Plots

Shared hyperparameters:

	value
algorithm	kd_tree

KMeans_tall vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_tall	fit	0.578	0.008	1000000	1000000	2	full	k-means++	30	3	1	0.0	30.0	NaN	30.0	NaN	0.392	0.023	1.476	0.090	See
1	KMeans_tall	predict	0.000	0.000	1000000	1	2	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.281	2.227	See
2	KMeans_tall	predict	0.000	0.000	1000000	1000	2	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.149	1.898	See
3	KMeans_tall	fit	0.485	0.020	1000000	1000000	2	full	random	30	3	1	0.0	24.0	NaN	30.0	NaN	0.361	0.015	1.344	0.078	See
4	KMeans_tall	predict	0.000	0.000	1000000	1	2	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.187	1.850	See
5	KMeans_tall	predict	0.000	0.000	1000000	1000	2	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.779	1.357	See
6	KMeans_tall	fit	6.310	0.079	1000000	1000000	100	full	k-means++	30	3	1	0.0	30.0	NaN	30.0	NaN	3.035	0.013	2.079	0.028	See
7	KMeans_tall	predict	0.000	0.000	1000000	1	100	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.514	1.095	See
8	KMeans_tall	predict	0.001	0.000	1000000	1000	100	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.842	0.988	See
9	KMeans_tall	fit	5.772	0.007	1000000	1000000	100	full	random	30	3	1	0.0	30.0	NaN	30.0	NaN	2.891	0.011	1.996	0.008	See
10	KMeans_tall	predict	0.000	0.000	1000000	1	100	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.968	1.736	See
11	KMeans_tall	predict	0.001	0.000	1000000	1000	100	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.051	1.196	See

Plots

Shared hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

KMeans_short vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_short	fit	0.276	0.012	10000	10000	2	full	k-means++	30	300	1	0.0	21.0	NaN	30.0	NaN	0.093	0.003	2.962	0.154	See
1	KMeans_short	predict	0.000	0.000	10000	1	2	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.990	1.727	See
2	KMeans_short	predict	0.001	0.000	10000	1000	2	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	1.158	0.399	See
3	KMeans_short	fit	0.107	0.001	10000	10000	2	full	random	30	300	1	0.0	30.0	NaN	30.0	NaN	0.041	0.001	2.644	0.046	See
4	KMeans_short	predict	0.000	0.000	10000	1	2	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.030	1.800	See
5	KMeans_short	predict	0.001	0.000	10000	1000	2	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	1.064	0.346	See
6	KMeans_short	fit	0.727	0.023	10000	10000	100	full	k-means++	30	300	1	0.0	20.0	NaN	18.0	NaN	0.321	0.019	2.270	0.152	See
7	KMeans_short	predict	0.000	0.000	10000	1	100	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.991	1.292	See
8	KMeans_short	predict	0.003	0.002	10000	1000	100	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	2.792	1.958	See
9	KMeans_short	fit	0.199	0.023	10000	10000	100	full	random	30	300	1	0.0	26.0	NaN	24.0	NaN	0.147	0.019	1.354	0.234	See
10	KMeans_short	predict	0.000	0.000	10000	1	100	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.961	1.278	See
11	KMeans_short	predict	0.006	0.002	10000	1000	100	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	5.315	2.208	See

Plots

Shared hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	30
n_init	1
tol	0.0

LogisticRegression vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	n_iter	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	LogisticRegression	fit	11.282	0.047	1000000	1000000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	11.086	0.016	1.018	0.005	See
1	LogisticRegression	predict	0.000	0.000	1000000	1	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.000	0.000	0.349	0.424	See
2	LogisticRegression	predict	0.000	0.000	1000000	1000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.000	0.000	0.849	0.462	See
3	LogisticRegression	fit	0.860	0.018	1000	1000	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[28]	0.780	0.025	1.102	0.042	See
4	LogisticRegression	predict	0.000	0.000	1000	1	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.001	0.001	0.084	0.118	See
5	LogisticRegression	predict	0.002	0.000	1000	100	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.003	0.000	0.563	0.145	See

Plots

Shared hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

Ridge vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	n_iter	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	Ridge	fit	1.711	0.044	100000	100000	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.834	0.003	2.052	0.054	See
1	Ridge	predict	0.000	0.000	100000	1	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.665	0.791	See
2	Ridge	predict	0.001	0.000	100000	1000	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.001	0.000	0.801	0.458	See
3	Ridge	fit	1.154	0.011	1000000	1000000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.221	0.001	5.229	0.056	See
4	Ridge	predict	0.000	0.000	1000000	1	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.637	0.761	See
5	Ridge	predict	0.000	0.000	1000000	1000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.725	0.609	See

Plots

Shared hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	False
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

Environment information

{
  "system_info": {
    "python": "3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1046-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "0.24.1",
    "numpy": "1.20.2",
    "scipy": "1.6.2",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": null,
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.12.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.12",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}